A Bayesian Approach to Learning 3D Representations of Dynamic Environments

  • Ralf KästnerEmail author
  • Nikolas Engelhard
  • Rudolph Triebel
  • Roland Siegwart
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 79)


We propose a novel probabilistic approach to learning spatial representations of dynamic environments from 3D laser range measurements. Whilst most of the previous techniques developed in robotics address this problem by computationally expensive tracking frameworks, our method performs in real-time even in the presence of large amounts of dynamic objects. The computer vision community has provided comparable methods for learning foreground activity patterns in images. However, these methods generally do not account well for the uncertainty involved in the sensing process. In this paper, we show that the problem of detecting occurrences of non-stationary objects in range readings can be solved online under the assumption of a consistent Bayesian framework. Whilst the model underlying our framework naturally scales with the complexity and the noise characteristics of the environment, all parameters involved in the detection process obey a clean probabilistic interpretation. When applied to real-world urban settings, the results produced by our approach appear promising and may directly be applied to solve map building, localization, or robot navigation problems.


Ground Truth Mobile Robot Gaussian Mixture Model Dynamic Environment Dynamic Object 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Biber, P., Duckett, T.: Dynamic maps for long-term operation of mobile service robots. In: Proc. of Robotics: Science and Systems, RSS (2005)Google Scholar
  2. 2.
    Bishop, C., et al.: Pattern Recognition and Machine Learning, pp. 94–97. Springer, New York (2006)zbMATHGoogle Scholar
  3. 3.
    Burgard, W., Stachniss, C., Hahnel, D.: Mobile robot map learning from range data in dynamic environments. STAR, vol. 35 (2007)Google Scholar
  4. 4.
    Hershey, J., Olsen, P.: Approximating the Kullback Leibler divergence between Gaussian mixture models. In: Proc. of The International Conference on Acoustics, Speech, and Signal Processing (ICASSP), vol. 4, pp. 317–320 (2007)Google Scholar
  5. 5.
    Hou, S., Galata, A.: Robust estimation of Gaussian mixtures from noisy input data. In: Proc. of The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8 (2008)Google Scholar
  6. 6.
    Jensen, B., Philippsen, R., Siegwart, R.: Motion detection and path planning in dynamic environments. In: Workshop Proceedings Reasoning with Uncertainty in Robotics, International Joint Conference on Artificial Intelligence, IJCAI (2003)Google Scholar
  7. 7.
    Kaestner, R., Thrun, S., Montemerlo, M., Whalley, M.: A non-rigid approach to scan alignment and change detection using range sensor data. In: Field and Service Robotics. STAR, 25th edn., pp. 179–194. Springer (2006)Google Scholar
  8. 8.
    Lee, D., Hull, J., Erol, B.: A Bayesian framework for Gaussian mixture background modeling. In: Proc. of The IEEE International Conference on Image Processing, vol. 3, pp. 973–976 (2003)Google Scholar
  9. 9.
    Lerner, U.: Hybrid Bayesian Networks for Reasoning about Complex Systems. PhD thesis, Stanford University (2002)Google Scholar
  10. 10.
    Luber, M., Arras, K., Plagemann, C., Burgard, W.: Classifying dynamic objects: An unsupervised learning approach. In: Robotics: Science and Systems IV, p. 270 (2009)Google Scholar
  11. 11.
    Roy, N., Burgard, W., Fox, D., Thrun, S.: Coastal navigation: Mobile robot navigation with uncertainty in dynamic environments. In: IEEE International Conference on Robotics and Automation, pp. 35–40. Citeseer (1999)Google Scholar
  12. 12.
    Schulz, D., Burgard, W.: Probabilistic state estimation of dynamic objects with a moving mobile robot. Robotics and Autonomous Systems 34(2-3), 107–115 (2001)zbMATHCrossRefGoogle Scholar
  13. 13.
    Sheikh, Y., Shah, M.: Bayesian object detection in dynamic scenes. In: Proc. of The IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, p. 74 (2005)Google Scholar
  14. 14.
    Stauffer, C., Grimson, W.: Learning patterns of activity using real-time tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 747–757 (2000)CrossRefGoogle Scholar
  15. 15.
    Surmann, H., Nüchter, A., Hertzberg, J.: An autonomous mobile robot with a 3D laser range finder for 3D exploration and digitalization of indoor environments. Journal of Robotics and Autonomous Systems (JRAS) 45(3-4) (2003)Google Scholar
  16. 16.
    Triebel, R., Pfaff, P., Burgard, W.: Multi-level surface maps for outdoor terrain mapping and loop closing. In: Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, IROS (2006)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2014

Authors and Affiliations

  • Ralf Kästner
    • 1
    Email author
  • Nikolas Engelhard
    • 2
  • Rudolph Triebel
    • 1
  • Roland Siegwart
    • 1
  1. 1.Autonomous Systems LabETH ZurichZurichSwitzerland
  2. 2.University of FreiburgFreiburgGermany

Personalised recommendations